academia/machinelearning/ml_course/KNN-手写体数字数据集

Posted on

KNN-手写体数字数据集

1
2
3
4
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
1
2
3
digits = datasets.load_digits()
X = digits.data
X.shape
(1797, 64)
1
2
y = digits.target
y.shape
(1797,)
1
2
3
one = X[100]
one = one.reshape(8, 8)
one
array([[ 0.,  0.,  0.,  2., 13.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  8., 15.,  0.,  0.,  0.],
       [ 0.,  0.,  5., 16.,  5.,  2.,  0.,  0.],
       [ 0.,  0., 15., 12.,  1., 16.,  4.,  0.],
       [ 0.,  4., 16.,  2.,  9., 16.,  8.,  0.],
       [ 0.,  0., 10., 14., 16., 16.,  4.,  0.],
       [ 0.,  0.,  0.,  0., 13.,  8.,  0.,  0.],
       [ 0.,  0.,  0.,  0., 13.,  6.,  0.,  0.]])
1
plt.imshow(one)
<matplotlib.image.AxesImage at 0x1a203a9ad0>

png

分离训练测试集

1
2
from sklearn.model_selection import train_test_split
train_test_split?
Signature: train_test_split(*arrays, **options)
Docstring:
Split arrays or matrices into random train and test subsets

Quick utility that wraps input validation and
``next(ShuffleSplit().split(X, y))`` and application to input data
into a single call for splitting (and optionally subsampling) data in a
oneliner.

Read more in the :ref:`User Guide <cross_validation>`.

Parameters
----------
*arrays : sequence of indexables with same length / shape[0]
    Allowed inputs are lists, numpy arrays, scipy-sparse
    matrices or pandas dataframes.

test_size : float, int or None, optional (default=None)
    If float, should be between 0.0 and 1.0 and represent the proportion
    of the dataset to include in the test split. If int, represents the
    absolute number of test samples. If None, the value is set to the
    complement of the train size. If ``train_size`` is also None, it will
    be set to 0.25.

train_size : float, int, or None, (default=None)
    If float, should be between 0.0 and 1.0 and represent the
    proportion of the dataset to include in the train split. If
    int, represents the absolute number of train samples. If None,
    the value is automatically set to the complement of the test size.

random_state : int, RandomState instance or None, optional (default=None)
    If int, random_state is the seed used by the random number generator;
    If RandomState instance, random_state is the random number generator;
    If None, the random number generator is the RandomState instance used
    by `np.random`.

shuffle : boolean, optional (default=True)
    Whether or not to shuffle the data before splitting. If shuffle=False
    then stratify must be None.

stratify : array-like or None (default=None)
    If not None, data is split in a stratified fashion, using this as
    the class labels.

Returns
-------
splitting : list, length=2 * len(arrays)
    List containing train-test split of inputs.

    .. versionadded:: 0.16
        If the input is sparse, the output will be a
        ``scipy.sparse.csr_matrix``. Else, output type is the same as the
        input type.

Examples
--------
>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> X, y = np.arange(10).reshape((5, 2)), range(5)
>>> X
array([[0, 1],
       [2, 3],
       [4, 5],
       [6, 7],
       [8, 9]])
>>> list(y)
[0, 1, 2, 3, 4]

>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, test_size=0.33, random_state=42)
...
>>> X_train
array([[4, 5],
       [0, 1],
       [6, 7]])
>>> y_train
[2, 0, 3]
>>> X_test
array([[2, 3],
       [8, 9]])
>>> y_test
[1, 4]

>>> train_test_split(y, shuffle=False)
[[0, 1, 2], [3, 4]]
File:      /Applications/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py
Type:      function
1
2
3
4
5
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)
(1437, 64)
(1437,)
(360, 64)
(360,)

测试

1
KNNClf = KNeighborsClassifier(n_neighbors=3)
1
KNNClf.fit(X_train, y_train)
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,
                     weights='uniform')
1
2
res = KNNClf.predict(X_test)
res
array([3, 4, 8, 5, 5, 7, 0, 3, 1, 4, 7, 5, 5, 8, 0, 7, 4, 7, 1, 7, 9, 4,
       4, 7, 6, 1, 2, 9, 1, 3, 3, 3, 7, 0, 7, 0, 2, 8, 9, 1, 1, 5, 4, 8,
       9, 0, 4, 9, 4, 9, 7, 2, 7, 3, 3, 4, 1, 9, 9, 9, 0, 4, 0, 6, 1, 0,
       0, 3, 6, 2, 3, 2, 8, 5, 9, 3, 1, 1, 6, 9, 8, 1, 2, 3, 2, 6, 8, 8,
       4, 6, 8, 6, 3, 9, 2, 8, 3, 6, 5, 7, 1, 7, 3, 8, 8, 8, 0, 0, 9, 1,
       9, 8, 5, 1, 1, 0, 1, 6, 5, 1, 7, 6, 5, 7, 7, 2, 2, 7, 3, 1, 9, 5,
       9, 5, 5, 3, 8, 4, 9, 5, 4, 6, 0, 5, 4, 8, 6, 1, 2, 8, 0, 9, 0, 9,
       7, 9, 7, 0, 2, 8, 2, 4, 0, 6, 2, 6, 7, 5, 6, 3, 8, 8, 0, 3, 2, 0,
       6, 1, 0, 6, 0, 5, 9, 3, 3, 0, 4, 0, 4, 2, 4, 9, 0, 6, 7, 4, 6, 5,
       9, 7, 2, 2, 3, 3, 0, 3, 9, 4, 9, 8, 5, 6, 9, 0, 1, 3, 5, 0, 5, 1,
       6, 4, 6, 6, 6, 7, 9, 1, 0, 7, 6, 6, 7, 8, 0, 3, 8, 5, 6, 8, 1, 3,
       0, 3, 6, 0, 3, 5, 6, 7, 0, 6, 9, 7, 0, 0, 2, 1, 6, 6, 9, 1, 6, 9,
       8, 7, 0, 2, 5, 1, 8, 6, 4, 3, 8, 2, 9, 2, 8, 4, 6, 4, 8, 9, 3, 1,
       1, 5, 0, 7, 8, 2, 3, 8, 4, 7, 7, 7, 7, 9, 4, 1, 7, 0, 2, 9, 1, 8,
       2, 4, 1, 0, 7, 5, 6, 0, 7, 1, 1, 5, 7, 1, 1, 6, 5, 6, 2, 2, 3, 9,
       7, 5, 3, 1, 5, 9, 9, 2, 5, 1, 6, 8, 2, 2, 4, 2, 1, 0, 6, 1, 2, 9,
       7, 5, 3, 5, 6, 1, 8, 1])
1
y_test
array([3, 4, 8, 5, 5, 7, 0, 3, 1, 4, 7, 5, 5, 8, 0, 7, 4, 7, 1, 7, 9, 4,
       4, 7, 6, 1, 2, 9, 1, 3, 3, 3, 7, 0, 7, 0, 2, 8, 9, 1, 1, 5, 4, 8,
       9, 0, 4, 9, 4, 9, 7, 2, 7, 8, 3, 4, 1, 9, 9, 9, 0, 4, 0, 6, 1, 0,
       0, 3, 6, 2, 3, 2, 8, 5, 9, 3, 1, 1, 6, 9, 8, 1, 2, 3, 2, 6, 8, 8,
       4, 6, 8, 6, 3, 9, 2, 8, 3, 6, 5, 7, 1, 7, 9, 8, 8, 8, 0, 0, 9, 1,
       4, 8, 5, 1, 1, 0, 1, 6, 5, 1, 7, 6, 5, 7, 7, 2, 2, 7, 3, 1, 9, 5,
       9, 5, 5, 3, 8, 4, 9, 5, 4, 6, 0, 5, 4, 8, 6, 1, 2, 8, 0, 9, 0, 9,
       7, 9, 7, 0, 2, 8, 2, 4, 0, 6, 2, 6, 7, 5, 6, 3, 8, 8, 0, 3, 2, 0,
       6, 1, 0, 6, 0, 5, 9, 3, 3, 0, 4, 0, 4, 2, 4, 9, 0, 6, 7, 4, 6, 5,
       9, 7, 2, 2, 3, 3, 0, 3, 9, 4, 9, 8, 5, 6, 9, 0, 1, 3, 5, 0, 5, 1,
       6, 4, 6, 6, 6, 7, 9, 1, 0, 7, 6, 6, 7, 8, 0, 3, 8, 5, 6, 8, 1, 3,
       0, 3, 6, 0, 3, 5, 6, 7, 0, 6, 9, 7, 0, 0, 2, 1, 6, 6, 9, 1, 6, 9,
       8, 7, 0, 2, 5, 1, 8, 6, 4, 3, 8, 2, 9, 2, 8, 4, 6, 4, 8, 9, 3, 1,
       1, 5, 0, 7, 8, 2, 3, 8, 4, 7, 7, 7, 7, 7, 4, 1, 3, 0, 2, 9, 1, 8,
       2, 4, 1, 0, 7, 5, 6, 0, 7, 1, 1, 5, 7, 1, 1, 6, 5, 6, 2, 2, 3, 9,
       7, 5, 3, 1, 5, 9, 9, 2, 5, 1, 6, 8, 2, 2, 4, 2, 1, 0, 6, 1, 2, 9,
       7, 5, 3, 5, 6, 1, 8, 8])
1
2
num = sum(res == y_test)
num
354
1
print('accuracy ', num / len(y_test))
accuracy  0.9833333333333333

使用sklearn中封装的计算精确度的方法

1
from sklearn.metrics import accuracy_score as accuracy
1
accuracy(res, y_test)
0.9833333333333333
1
2